Method and system employing graphical electric load categorization to identify one of a plurality of different electric load types

ABSTRACT

A system for different electric loads includes sensors structured to sense voltage and current signals for each of the different electric loads; a hierarchical load feature database having a plurality of layers, with one of the layers including a plurality of different load categories; and a processor. The processor acquires voltage and current waveforms from the sensors for a corresponding one of the different electric loads; maps a voltage-current trajectory to a grid including a plurality of cells, each of which is assigned a binary value of zero or one; extracts a plurality of different features from the mapped grid of cells as a graphical signature of the corresponding one of the different electric loads; derives a category of the corresponding one of the different electric loads from the database; and identifies one of a plurality of different electric load types for the corresponding one of the different electric loads.

This invention was made with Government support under DE-EE0003911awarded by the Department of Energy National Energy TechnologyLaboratory. The Government has certain rights in this invention.

BACKGROUND

1. Field

The disclosed concept pertains generally to electric loads and, moreparticularly, to methods of identifying different types of electricloads. The disclosed concept also pertains to systems for identifyingdifferent types of electric loads.

2. Background Information

Electric loads in commercial and residential buildings consumed about75% of total electricity in the U.S. in 2012. However, a large portionof this electricity use has been wasted, and the management of thisusage has often been overlooked. Many electric appliances with anexternal power supply, a remote control, a continuous display or abattery charger continuously draw power in an off or standby mode.Electric loads with external power supplies are also called plugged-inelectric loads (PELs) (or miscellaneous electric loads in somecontexts). PELs is one of the major load categories and accounts formore use than any other end-use service, such as heating andventilation.

Standby power in the U.S. accounts for over 100 billion kWh and costsover $10 billion annually. As much as 75% of this cost can be saved byproper energy management. In order to achieve theNet-Zero-Energy-Building goals defined by the Department of Energy (DOE)for residential buildings by 2020 and for commercial buildings by 2025,the effective monitoring and management of PELs needs to be considered.Knowing the type of PELs is essential to enabling an effective solution.

Since the introduction of non-intrusive load monitoring (NILM) in the1980s, numerous prior proposals have sought to develop various NILMsolutions. A wide-range of known solutions is disclosed by Du et al., “Areview of identification and monitoring methods for electric loads incommercial and residential buildings,” Proc. 2010 IEEE Energy ConversionConf. and Expo., 2010, pp. 4527-33.

A load identification system typically consists of several modulesincluding data acquisition, data processing, event detection, featureextraction, and identity indication. The identity indication modulecompares the extracted features with a database of features of knownloads and identifies unknown loads based on pre-defined rules, such asmaximum similarity or learning results of artificial neural networks(ANNs).

The performance of almost all existing load identification methodshighly depends on the electrical signatures of loads, which are definedto be “an electrical expression that a load device or appliancedistinctly possesses”. The objective is to extract useful features thatcan uniquely distinguish the individual PELs types or classes within apredetermined load set.

Many feature extraction methods have been proposed. For example, forsteady state feature exploration, real and reactive power is utilized toidentify load types. Also, peak current, average current and RMS currentvalues can be used for load identification. Current harmonics areapplied as the core features for identification to mainly address thoseloads with a nonlinear power supply. Further, a voltage-current (V-I)trajectory modeling method for load identification uses purely graphicalshape features of the V-I trajectory of each load. Also, some transientstate features, such as instantaneous admittance curves and transientpower curves, can be employed.

The development of feature extraction and the assignment of each loadtype to a corresponding load group and sub-group has been purelydata-driven. Even though many prior proposals demonstrate thatsatisfactory performance can be achieved by selecting a proper set offeatures for a targeted load set, there are no known guidelines to drivean optimized feature selection, and there is potentially a redundancy ofinformation in any set of features. Moreover, the identificationperformance usually depends on the specific load set under study. It isbelieved that how well the performance of the developed classifier canbe generalized to other load sets has not yet been addressed, and thatthere does not exist a set of electrical signatures such that every loadcan have a “distinct” expression.

Due to the complexity and nuances of devices and appliances, it is oftenchallenging, if not impossible, to distinguish between loads that usethe same interface circuit to a power line. For example, those PELsusing a standardized direct current (DC) power supply with currentharmonic reduction, such as DVD players, cable or satellite set-topboxes, and PC monitors, present very similar electrical signatures, andare not distinguishable by only using the steady state features. Hence,a truly meaningful load categorization method is often still desired.

There is room for improvement in methods of identifying differentelectric load types.

There is also room for improvement in systems for identifying differentelectric load types.

SUMMARY

These needs and others are met by embodiments of the disclosed conceptwhich map a voltage-current trajectory to a grid including a pluralityof cells each having a binary value; extract a plurality of differentfeatures from the mapped grid of cells as a graphical signature of acorresponding one of a plurality of different electric loads; derive acategory of the corresponding one of the different electric loads from ahierarchical load feature database; and identify one of a plurality ofdifferent electric load types for the corresponding one of the differentelectric loads.

In accordance with one aspect of the disclosed concept, a system for aplurality of different electric loads comprises: a plurality of sensorsstructured to sense a voltage signal and a current signal for each ofthe different electric loads; a hierarchical load feature databasecomprising a plurality of layers, with one of the layers including aplurality of different load categories; and a processor structured to:

acquire a voltage waveform and a current waveform from the sensors for acorresponding one of the different electric loads; map a voltage-currenttrajectory to a grid including a plurality of cells, each of the cellsbeing assigned a binary value of zero or one; extract a plurality ofdifferent features from the mapped grid of cells as a graphicalsignature of the corresponding one of the different electric loads;derive a category of the corresponding one of the different electricloads from the hierarchical load feature database; and identify one of aplurality of different electric load types for the corresponding one ofthe different electric loads.

As another aspect of the disclosed concept, a method of identifying loadtypes for a plurality of different electric loads, the methodcomprising: sensing a voltage signal and a current signal for each ofthe different electric loads; providing a hierarchical load featuredatabase comprising a plurality of layers, with one of the layersincluding a plurality of different load categories; acquiring a voltagewaveform and a current waveform for a corresponding one of the differentelectric loads; mapping a voltage-current trajectory to a grid includinga plurality of cells, each of the cells being assigned a binary value ofzero or one; extracting a plurality of different features from themapped grid of cells as a graphical signature of the corresponding oneof the different electric loads; deriving a category of thecorresponding one of the different electric loads from the hierarchicalload feature database; and identifying one of a plurality of differentelectric load types for the corresponding one of the different electricloads.

BRIEF DESCRIPTION OF THE DRAWINGS

A full understanding of the disclosed concept can be gained from thefollowing description of the preferred embodiments when read inconjunction with the accompanying drawings in which:

FIGS. 1A-1G are plots of current versus voltage and normalized currentversus normalized voltage for V-I trajectories of representative loadsin seven load categories in accordance with embodiments of the disclosedconcept.

FIGS. 2A-2D are plots of normalized current versus normalized voltagefor the V-I trajectories of four particular example loads.

FIG. 3 is a mapping of a plot of a V-I trajectory to a binary cell gridin accordance with embodiments of the disclosed concept.

FIG. 4A is a plot of sampled voltage versus discrete sample for aparticular load including an average of the maximal and minimal voltagevalues in accordance with an embodiment of the disclosed concept.

FIG. 4B is a plot of sampled current versus discrete sample for theparticular load of FIG. 4A including an average of the maximal andminimal current values.

FIG. 4C is a plot of current versus voltage for the V-I trajectory ofthe particular load of FIG. 4A showing the averages of maximal andminimal voltage and current values.

FIG. 5A is a plot of sampled voltage versus discrete sample for aparticular load including a particular voltage sample in accordance withan embodiment of the disclosed concept.

FIG. 5B is a plot of sampled current versus discrete sample for theparticular load of FIG. 5A including a particular current sample.

FIG. 5C is a plot of current versus voltage for the V-I trajectory ofthe particular load of FIG. 5A showing the particular voltage andcurrent sample.

FIGS. 6A and 6B are example plots of binary cell grids in accordancewith embodiments of the disclosed concept.

FIG. 7 is a plot of a self-crossing intersection contained by a V-Itrajectory in accordance with an embodiment of the disclosed concept.

FIG. 8 is a block diagram of a system employing graphical electric loadcategorization to identify one of a plurality of different electric loadtypes in accordance with embodiments of the disclosed concept.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

As employed herein, the term “number” shall mean one or an integergreater than one (i.e., a plurality).

As employed herein, the term “processor” shall mean a programmableanalog and/or digital device that can store, retrieve, and process data;a computer; a digital signal processor; a controller; a workstation; apersonal computer; a microprocessor; a microcontroller; a microcomputer;a central processing unit; a mainframe computer; a mini-computer; aserver; a networked processor; or any suitable processing device orapparatus.

In accordance with the disclosed concept, an electric loadcategorization by graphical methods examines the relationship betweenphysical electrical circuits and their corresponding features. With athorough understanding of electric appliances, feature extraction isdriven by an understanding of the relationship between different steadystate current waveforms and their corresponding circuit topologies, andthe resultant features are defined in a load model-driven manner ratherthan through mere data mining (also known as a purely data-drivenapproach). Electrical signatures of loads are extracted from V-Itrajectories. The V-I trajectories are first mapped to a grid of cells,each of which is assigned a binary value. A set of simple, but efficientfeatures, are then extracted from the mapped cell grid with binaryvalues. This established relation is very helpful to optimize thefeature space and to define simpler features. The disclosed mapping tocell grids with binary values aims at bypassing a discrete FourierTransform (DFT) operation and reducing the required computationalresources. It also provides a description of the limitation of steadystate features used in prior proposals.

U.S. Patent Application Pub. No. 2013/0138669, entitled: “System AndMethod Employing A Hierarchical Load Feature Database To IdentifyElectric Load Types Of Different Electric Loads”, which is incorporatedby reference herein, discloses a system and method that employs ahierarchical load feature database and classification structure asmodel-driven guidance for optimized feature selections.

The disclosed concept fits in the Level-1 categorization in thehierarchical load identification framework as disclosed by Pub. No.2013/0138669, and focuses on the steady state feature extraction.Because of the potential limitation of using only the steady statefeatures, a finer granularity for load identification can be achieved byintroducing the load identification/categorization in Level-2 andLevel-3 in the hierarchical load identification framework of Pub. No.2013/0138669.

A hierarchical load feature database comprises three layers, althoughmore than three layers can be employed. The first layer or level(Level-1) is the load category, the second layer or level (Level-2) isthe load sub-category, and the third layer or level (Level-3) is theload type, which includes a plurality of different load types.

Non-limiting examples of load categories of the first level includeresistive loads, reactive loads, nonlinear with power factor correction,nonlinear without power factor correction, nonlinear with transformer,nonlinear with phase angle control, and complex structure.

Non-limiting examples of load sub-categories of the second level includeresistive loads, such as lighting tools, kitchen appliances and personalcare appliances; reactive loads, such as linear reactive loads andnonlinear with machine saturations; nonlinear with power factorcorrection, such as large monitors, television equipment and other largeconsumer electronic devices; nonlinear without power factor correction,such as imaging equipment, small monitors and televisions, personalcomputers (PCs), electronic loads with a battery charger, lighting loadsand other small electronic devices; nonlinear with transformer, such assmall electronics without a battery charger and others with a batterycharger; and complex structures, such as a microwave oven.

A few non-limiting examples of load types of the third level areincandescent lamps (<100 W) for lighting tools, and a bread toaster, aspace heater and other appliances for kitchen and personal careappliances.

Load Categorization by Front-End Electronic Circuit Topologies

The electric signals, i.e., voltage and current waveforms, of PELsduring steady state are directly related to the circuit topology oftheir front-end power supply units. The first level, i.e. Level-1 inTable 1, below, includes seven load categories: resistive loads (R);reactive predominant loads (X); electronic loads (E-loads) with a powerfactor correction circuit (P); electronic loads without a power factorcorrection circuit (NP); linear power supply using transformer to boostvoltage (T); phase angle controllable loads (PAC); and complexstructures (M).

The majority of resistive loads (R) are used for heating, cooking andlighting. Non-limiting examples of such loads include space heaters,coffeemakers and incandescent lamps. For reactive loads (X), theappliances often consist of compressors, motors or chillers. The motorscommonly used for appliances are often small DC motors. Non-limitingexamples of such loads in this sub-category are fans, washers,refrigerators and shredders. The next two large groups of appliances areall electronic loads, denoted as categories P and NP in Table 1. Sincethe IEC Standard 61000-3-2 limits harmonic current level for all theloads with power above 75 watts, it can be assumed that a power factorcorrection (PFC) module is needed to meet this requirement. Therefore,category P refers to electronic loads with PFC. Personal computers over75 W, projectors, LCD TVs, LED TVs (working in the “high quality mode”),Plasma TVs, home theaters and game consoles, also all belong to CategoryP. In contrast, Category NP refers to electronic loads that do notutilize power factor correction techniques. Small devices, such ascellular telephone chargers, portable DVD players, adaptors of portableprinters, scanners, facsimile machines and multiple function devices(MFD) using ink-jet, PC monitors, LED TVs (operating in the energysaving mode) and PCs (operating in a low power mode) are major loads inthis sub-category. Loads in Category T refer to those low powerappliances that use linear DC power supplies with a relatively smalltransformer at the front-end. Battery chargers, paper punchers andstaplers are non-limiting examples of representative loads in thiscategory. Devices such as light dimmers that use thyristor phase anglevoltage control are listed in the PAC category. Category M includesappliances that often have relatively high power consumption, andmultiple electrical systems, such as microwave ovens and laser printers.Furthermore, category M loads also include PELs that operate at severaldifferent power levels and switch between these power levels repeatedlyduring usage. These PELs are programmed to operate in this repeatedswitching-mode manner because their functional performance may requirerepeated processes in a certain sequence. For example, most high volumeprinters have two print engines in a single device and are able to printboth sides of the paper in a single pass. A double-sided printing job isa repeated process of feeding a piece of paper, printing and rolling thepaper forward, holding the paper for the ink to dry, reversing the paperbackward to print on the other page, and feeding the next piece of paperfor fast printing. The two engines are programmed to operate indifferent combinations during this repeated process, and thesecombinations could fall into one or several categories listed above.

TABLE 1 Seven Load Categories by Front- Example Plug-in Loads EndElectronic Topologies under Each Category R: Resistive Loads R1:Incandescent Lamps (<100 W) R2: Space Heater R3: Bread Toaster R4:Coffee Machines- Other Kitchen Appliances X: Reactive Predominant LoadsX1: Fans X2: Refrigerator (any with chiller) X3: Vending Machine X4:Shredder P: E-loads with PFC P1: PC (Desktop/Laptop) (>75 W) P2:Projectors P3: Big TVs (LCD/LED) (>75 W) P4: Home Theater/Game Consoles(70-80 W) NP: E-loads without PFC nP1: PC (Laptop) (<75 W) nP2: Charger(any with battery) nP3: Other Small Electronic Devices nP4: FL/CFL nP5:Portable MFD/Printer/Scanner nP6: PC Monitors T: Linear Loads T1: SmallElectronics (e.g., Stapler) T2: AA Battery Charger PAC: Phase AngleControlled PAC1: Dimmer Loads PAC2: Others with Thyristor ControlledRectifier M: Complex Structure M1: Microwave Oven M2:Printer/Copier/Facsimile Machine/MFDTypical VI Trajectories of Plug-in Load Categories

FIGS. 1A-1G show plots 2, 6, 10, 14, 18, 22, 26 of current versusvoltage and plots 4, 8, 12, 16, 20, 24, 28 of normalized current versusnormalized voltage for the V-I trajectories of representative loads inthe above load categories R, X, P, NP, T, PAC and M, respectively. Sinceonly the Level-1 categories are considered in the first step, all of theloads present very distinct characteristics from one category toanother. By applying a relatively very simple feature space, it isfeasible to realize the Level-1 load category identification, whichmakes it a potential solution for a low-cost, embedded systemimplementation of plug-load identification.

From FIGS. 1A-1G, it can be observed that the normalized V-Itrajectories from the different load categories appear different. Aprior proposal based on 126 sets of operating data of different PELtypes and modes summarizes that there are eight shape signatures thatcan be considered to describe the V-I trajectory: asymmetry, loopingdirection, area, curvature of the mean line, self-intersection, slope ofmiddle segment, area of left and right segments, and peak of middlesegment. However, calculating these graphical signatures still requiresmuch computational resources since the entire V-I trajectory needs to betraversed in a certain order or direction. Also, these signatures aredesigned for a taxonomy of loads that is similar to the load groupsdefined by IEC Standard 61000-3-2. Therefore, they are not suitable forthe proposed seven load categories. Furthermore, as will be discussed,these signatures extracted from V-I trajectories cannot effectivelyhandle the diversity within each type of PEL and the similarity betweendifferent types of PELs. Instead, for the purpose of loadidentification, the disclosed concept employs a different set ofsignatures that can be extracted from V-I trajectories. Furthermore,such signatures of different categories are distinct.

Constraints of Existing Graphical Load Signatures

The existing graphical load signatures discussed above are purely basedon shape features. However, different models of PELs within the samecategory can be equipped with similar (but not identical) front-endpower supply topology. Therefore, such PELs present similar (but notidentical) current waveforms as well as V-I trajectories. In this case,there can be significant differences in some of existing graphical loadsignatures, which are supposed to be identical as these PELs belong tothe same type or category. Moreover, some existing graphical signaturesmay no longer be true or useful.

Several example plots 30, 32, 34, 36 of normalized current versusnormalized voltage for the V-I trajectories of particular loads areshown in FIGS. 2A-2D, respectively. FIGS. 2A and 2B show the plots 30,32for the V-I trajectories of two portable fans (e.g., 32 inch and 9 inch,respectively). It can be observed that these two V-I trajectories have asimilar shape, but quite different area values of both the entire V-Itrajectory and the left and right segments as well as peak values ofmiddle segments. As another example, FIGS. 2C and 2D show the plots34,36 for the V-I trajectories of two flat-panel television sets (e.g.,an LED and an LCD, respectively). It can be observed that these two V-Itrajectories have a similar shape, but have quite different zerocrossing times (and, thus, different left, middle and right segmentvalues). Also, to determine the asymmetry, looping direction, and areaof the plot 34 of FIG. 2C is relatively complicated and needs arelatively large computation time due to the oscillation in the V-Itrajectory.

Binary Mapping from V-I Trajectories

FIG. 3 shows the mapping of a plot 38 of a V-I trajectory to a binarycell grid 40. In order to effectively handle the difference between theV-I trajectories of PELs within the same load category and reduce error,the disclosed concept first maps the V-I trajectory to a grid of cells.Each cell is assigned a binary number (i.e., zero or one). If the V-Itrajectory crosses a cell, then this cell is considered to be occupiedby this V-I trajectory, is assigned a digital value of 1, and is shown,for example, as being solid black in FIG. 3.

The binary cell grid 40 is a generalization of V-I trajectories. V-Itrajectories with similar, but not identical shapes, can have identicalmapped binary cell grids. This is because two V-I trajectories can passa cell along different paths, but the cell is yet considered as occupiedand is assigned a binary value of 1. The following defines a binary cellgrid mapping algorithm in accordance with the disclosed concept.

First, the voltage and current data is loaded assuming that there are atotal of K data points of the form (v_(k),i_(k)), wherein:

-   -   k=1, . . . , K; and    -   v_(k) and i_(k) are the voltage and current values of sample        data point k, respectively.

Second, the maximal and minimal values of the voltage and currentwaveforms, i.e., v_(max), v_(min), i_(max) and i_(min), are computedfrom:v _(max)=maxv _(k),v _(min)=minv _(k),i _(max)=maxi _(k),i _(min)=mini _(k),

${v_{0} = {\frac{1}{2}\left( {v_{\max} + v_{\min}} \right)}},{and}$${i_{0} = {\frac{1}{2}\left( {i_{\max} + i_{\min}} \right)}};$wherein:

-   -   v₀ and i₀ are both averages of the corresponding maximal and        minimal values of voltage and current, respectively, and which        form the central points of the cell grid.

Physically, the v₀ and i₀ values are the DC bias values of therespective voltage and current waveforms, which are usually introducedby the DC offset of the voltage and current sensors (not shown, but seesensors 106 of FIG. 8) and/or the asymmetry between positive andnegative half-cycles of the waveforms. In an ideal scenario, these DCoffsets are relatively very small, or even close to zero.

FIGS. 4A-4C show an example with a DC bias (i₀) of 0.032 A on thecurrent waveform (FIG. 4B), and a DC bias (v₀) of 0.7 V on the voltagewaveform (FIG. 4A). FIG. 4A shows a plot 42 of sampled voltage versusdiscrete sample for a particular load. FIG. 4B shows a plot 44 ofsampled current versus discrete sample for the particular load of FIG.4A. FIG. 4C shows a plot 46 of the current versus the voltage for theV-I trajectory of the particular load of FIG. 4A and includes a point 48for the v₀ and i₀ values.

Third, given a predefined parameter Δ, the width (or size) of the gridis defined and calculated by:

${\mathbb{d}v} = \frac{v_{\max} - v_{0}}{\Delta}$${\mathbb{d}i} = \frac{i_{\max} - i_{0}}{\Delta}$and generate the two sequences:{v ₀ −dv·Δ,v ₀ −dv·(Δ−1), . . . ,v ₀ , . . . ,v ₀ +dv·(Δ−1),v ₀ +dv·Δ},and{i ₀ −di·Δ,i ₀ −di·(Δ−1), . . . ,i ₀ , . . . ,i ₀ +di·(Δ−1),i ₀ +di·Δ}Here, both of these sequences have 2Δ+1 elements.

Fourth, define an N×N square cell grid,

wherein:N=2Δ+1.The (x^(th), y^(th)) cell is assigned with a positional value (v₀+dv·x,i₀+di·y) and a binary model value B_(x,y), which is initialized to be 0.

Fifth, load one-half line cycle of data points as shown in FIGS. 5A-5C.The one-half cycle of the voltage waveform 50 (FIG. 5A) and the one-halfcycle of the current waveform 52 (FIG. 5B) starts from the voltagezero-crossing point at 51V (FIG. 5A) with a positive gradient (i.e., thevoltage value crosses zero from a negative value to a positive value),and ends at another voltage zero-crossing point 53V (FIG. 5A) with anegative gradient (i.e., the voltage value crosses zero from a positivevalue to a negative value). Similar starting and ending points 51A,53A(FIG. 5B) for the current waveform 52 are shown in FIG. 5B. The startingpoints 51V,51A, the ending points 53V,53A, and one examplecurrent/voltage sample value (v_(k),i_(k)) 55 are shown in the voltageversus current plot 54 of FIG. 5C.

Sixth, starting with the first data point 51V,51A of the data pointsloaded in the previous step, which is denoted by (v₁ ^(h),i₁ ^(h)),execute the following loop:

  for every cell (Δ + 1, y), y = Δ + 1, Δ + 2, . . . , 2Δ + 1:  ${{if}\mspace{14mu}\left( {v_{1}^{h} - v_{0}} \right)} < {\frac{dv}{2}\mspace{14mu}{and}\mspace{14mu}\left( {i_{1}^{h} - \left( {i_{0} + {y \cdot {di}}} \right)} \right)} < \frac{di}{2}$  cell (Δ + 1, y) is occupied and B_(Δ+1, y) = 1;   cell (Δ + 1, y) isstored as the winner of (v₁ ^(h), i₁ ^(h));   BREAK;  end endIn the above execution loop, every cell, (Δ+1, y), for y=Δ+1, Δ+2, . . ., 2 Δ+1, in the grid is determined whether it is occupied by a certaindata point, (v₁ ^(h),i₁ ^(h)). If one cell is determined to be occupiedby this data point, then this cell is denoted as the winner for thisdata point. Once the winner cell is determined, the loop BREAKs (alsoknown as the loop being terminated) for this data point. If this datapoint is the first in the data sequence (i.e., half-cycle of data pointsfrom the fifth step), this step also marks the occupied cell as thestarting cell.

Seventh, the sixth step is repeated by searching and determining thecell occupancy for the remaining half-cycle data points from the fifthstep. To speed up the execution process, only, for example, the eightadjacent cells of a previous winner are considered for each searchingloop.

Eighth, repeat from the sixth step for a predetermined number (e.g.,without limitation, the number of data points in a half-cycle; tens orhundreds; any suitable number) of times.

The coefficient Δ defines the width of each cell and, thus, the numberof cells within the cell grid. The size of the grid should be chosenbased on different applications. If there are too many cells, then themapping of V-I trajectories to the binary cell grid may not effectivelyhandle the variance of similar V-I trajectories. However, the mappedbinary cell grid may not correctly represent the V-I trajectories if thenumber of cells is not sufficient.

Feature Extraction Based on Binary V-I Cell Grid

Besides reducing the error introduced by the difference between V-Itrajectories of PELs within the same load category, the mapping of V-Itrajectories onto binary cell grids can also reduce the effect ofdistortion, but preserve the graphical characteristics. For eachcategory of PELs, the disclosed concept employs a set of novelsignatures that can be directly identified from the binary cell grid.

FIGS. 6A and 6B graphically depict three key points or cells (P1, P2,P3) 56, 58, 60 and four key lines (L1, L2, L3, L4) 62, 64, 66, 68 asfeatures in a binary cell grid. The following gives an example of a setof eight features that can be used to identically represent each loadcategory:

(1) Feature1: the binary value of the left horizontal cell (1,Δ+1),marked as cell P1 56 in FIG. 6A, where the applicable values include: 0(cell-unoccupied), and 1 (cell-occupied);

(2) Feature2: the binary value of the central cell (Δ+1,Δ+1), marked ascell P2 58 in FIG. 6A, where the applicable values include: 0(cell-unoccupied), and 1 (cell-occupied);

(3) Feature3: the multiplication of anti-diagonal grid cell values,i.e., the multiplication of the binary values of all cells along thediagonal line (marked as line L2 64 in FIG. 6A) in the grid from thelower left corner to the upper right corner. This number is also ofbinary value and indicates whether the V-I trajectory is linear, or inother words, whether the V-I trajectory is aligned with the diagonalline. The applicable values of this Feature3 include: 1 (linear) whenthe V-I trajectory is of the shape of a straight line from the lowerleft corner to the upper right corner with the examples as shown in FIG.1A; and 0 (non-linear) when at least one of the anti-diagonal cells isnot occupied and the V-I trajectory is not a straight line with theexamples as shown in FIGS. 1B-1G;

(4) Feature4: the number of continuums of grid cells with value 1 withinall cells (Δ+1,[1:2Δ+1]), which indicates the number of intersections ofthe V-I trajectory and the base voltage v₀ line (marked as line L1 62 inFIG. 6A); the notation 1:2Δ+1 denotes all integers from 1 to 2Δ+1; theapplicable values include: 1 (one-cell), and 2 (two-cells);

(5) Feature5: whether there exists any self-crossing intersections ofthe V-I trajectory itself; the applicable values include: 0 (none), 1(one-intersection), 2 (two-intersections), and so forth;

(6) Feature6: the number of intersections of the V-I trajectory with the1.3v₀ line (marked as line L3 66 in FIG. 6A); the applicable valuesinclude: 1 (one-intersection), and 2 (two-intersections);

(7) Feature7: the existence of a central horizontal line-segment (markedas line L4 68 in FIG. 6B); this line L4 occupies 30% of the entirehorizontal line, where y=0 in the grid; the existence of such a line isdetermined if 50% of the line is overlapped with part of the V-Itrajectory; the applicable values include: 0 (no-horizontal-line), and 1(with-horizontal-line); and

(8) Feature8: the binary value of the top-middle cell (Δ+1, 1), markedas cell P3 60 in FIG. 6B; the applicable values include: 0(cell-unoccupied), and 1 (cell-occupied).

Number of Self-Crossing Intersections

The V-I trajectories of some electric loads cross-intersect themselves,as is shown, for example, in FIG. 1G. A prior proposal might suggestthat the number of self-crossing intersections contained by a V-Itrajectory could be related to the order of harmonics. For example, asimulated load with a significant 3^(rd) (or 5^(th)) harmonic componentin the current has two (or four) self-crossing intersections. However,it can also be caused by loads in Category M (i.e., loads with multipleindependent front-end power supply units). Therefore, the disclosedconcept employs a general, but yet low-cost, algorithm to determine thenumber of self-crossing intersections contained by a V-I trajectory, asshown in FIG. 7.

First, read one-half line cycle

$\text{(e.g.,}\frac{1}{120}\mspace{14mu}\text{second}\left. \quad \right)$of sampled data points [0⁻, 0₊], starting with the zero-crossing datapoint from negative voltage values to positive voltage values (denotedby 0⁻) and ending with the zero-crossing data point from positivevoltage values to negative voltage values (denoted by 0₊).

Second, for every data point j within the region [0⁻, peak₊], wherepeak₊ denotes a data point in [0⁻, 0₊] with the maximal positive voltagevalue, find the data point k whose voltage value is closest to point j.

Third, denote a data point j with voltage value v_(j) and current valuei_(j) by a vector

, and check whether values of the current of the data point sequence

and {k−1, k, k+1} are monotonically increasing; if yes, go to the fourthstep, below, and, if not, then repeat this third step starting with j+1.

Fourth, check whether data points k−1=(v_(k−1),i_(k−1)) andk+1=(v_(k+1),i_(k+1)) are on the different side of the line determinedby j⁻¹ =(v_(j−1),i_(j−1)) and j+1=(v_(j+1),i_(j)+1) using the followingcriterion:{( j+1− j−1)×( j+1− k−1)}·{( j+1− j−1)×( j+1− k+1)}0wherein:

× denotes the cross product; and

· denotes the dot product.

In other words, for any j and k, an instance when the criterion of thefourth step is satisfied is considered as being a self-crossingintersection.

Numerical Test Results

The disclosed concept can be employed in combination with the teachingsof any or all of: (1) U.S. Patent Application Pub. No. 2013/0138651,entitled: “System And Method Employing A Self-Organizing Map LoadFeature Database To Identify Electric Load Types Of Different ElectricLoads”; (2) U.S. Patent Application Pub. No. 2013/0138661, entitled:“System And Method Employing A Minimum Distance And A Load FeatureDatabase To Identify Electric Load Types Of Different Electric Loads”;and (3) U.S. patent application Ser. No. 13/597,324, filed Aug. 29,2012, entitled: “System And Method For Electric Load Identification AndClassification Employing Support Vector Machine”, all of which areincorporated by reference herein.

In accordance with the teachings of the disclosed concept, the resultantbinary V-I features extracted from the mapped cell grid with binaryvalues can be used as inputs to any or all of the classification andidentification systems disclosed in the above three patent applicationsto derive the category of the load under observation. With reference tothe hierarchical load identification architecture as disclosed in Pub.No. 2013/0138669, the disclosed concept can be applied to provide thefeatures needed by the Level-1 load category identification. Thecategorization of the loads can be conducted by applying a SupervisedSelf-Organizing Map (SSOM) or a self-organizing map (SOM) (also known asa self-organizing feature map (SOFM)) that is a type of unsupervisedartificial neural network that is trained using competitive learning toproduce a relatively low-dimensional (typically two-dimensional),discretized representation of the input space of training samples,called a map, as disclosed by Pub. No. 2013/0138651.

Test on Five Major Load Categories

Five of the load categories (i.e., R, X, NP, P and M) cover the majorityof existing PELs. The following discusses the success rate ofidentifying loads from these five load categories by using the firstfive features disclosed herein. The proposed graphical signatures frombinary mapping of V-I trajectories for these five categories of PELs areexpected to have values (where “X” means either 0 or 1) as shown inTable 2.

TABLE 2 Category Feature 1 Feature 2 Feature 3 Feature 4 Feature 5 R 0 11 1 0 X 0 0 0 2 0 NP 1 1 0 1 0 P 0 1 0 1 0 M 0 X 0 2 1 or more

For each category, a number of PELs are tested and each PEL isindependently tested 100 times. The result is shown in Table 3.

TABLE 3 Total Number Total Number Total Number of Correct SuccessCategory of Loads of Tests Results Rate R  6 600 597 99.5% X 10 1000 99199.1% P 15 1500 1493 99.5% NP 11 1100 1091 99.2% M  4 400 395 98.8%

In summary, the proposed graphical signatures from binary mapping of V-Itrajectories achieve an average of over a 99% accuracy rate. Theidentification of loads from Category M (i.e., loads with multipleindependent front-end power supply units) has the lowest accuracy inTable 3. This is mainly due to the wide diversity of loads in thiscategory.

Test on all Seven Load Categories

In this test, all seven load categories are considered. The proposedgraphical signatures from binary mapping of V-I trajectories for theseven categories of PELs are expected to have values as shown in Table4.

TABLE 4 Category Feature 1 Feature 2 Feature 3 Feature 4 Feature 5Feature 6 Feature 7 Feature 8 R 0 1 1 1 0 1 0 0 X 0 0 0 2 0 2 0 0 NP 1 10 1 0 1 1 0 P 0 1 X 1 0 1 1 0 M 0 X 0 2 1 or more 2 0 0 T 0 0 0 2 0 2 01 PAC X 1 1 1 0 2 1 0

In this test, a total of 20 load types (with one to seven load modelsfor each load type) are tested. For each data set, about 900 to about3000 V-I trajectories are selected and mapped to a 64×64 cell grid. Theresults are shown in Table 5.

TABLE 5 Target Total Load Total Number Success Category Load Type Modelsof Tests Rate (%) NP Battery Charger 1 3000 83.4 DVD Player 4 3000 100Desktop Computer 2 3000 99.8 LCD Monitor 7 3000 99.5 Printer 1 3000 99.9Electronic Circuit Board 1 3000 98.7 P LCD TV 8 3000 98.5 LED TV 3 300099.2 Plasma TV 2 3000 99 Multi Function Device 3 3000 93 Projector 43000 99.9 Complex Microwave Oven 4 1800 99 M R Space Heater 4 1800 93Coffee Maker 2 1800 98 Incandescent Lamp 4 1800 99.2 Electric Skillet 21800 98.6 T Stapler 1 1800 98.9 Adapters 5 1800 100 X Fan 5 3600 98.5Refrigerator 4 3600 100 Water Dispenser 1 3600 100 Shredder 2 3600 65PAC Incandescent Lamp with 1 1800 50 Dimmer

The testing results validate that the proposed graphical signatures frombinary mapping of V-I trajectories can achieve an average of over a 90%accuracy rate with a relatively large load set and with seven targetload categories. The major failure cases are from some PAC loads wherethe phase angle is less than 90°, which makes the load's featuressimilar to what are expected for resistive loads, such that they aremistakenly categorized as being in the R-category. Increasing thesampling rate of the sensed voltage and current signals may help toimprove the performance, although a trade-off should be considered interms of memory space availability and computation burden. At the sametime, from the application point of view, if an incandescent lamp withdimmer with a relatively small phase angle is identified as a resistiveload, then the resulting categorization will still be acceptable.

SUMMARY

FIG. 8 shows a system 100 for different electric loads 102, 103, 104.The system includes sensors 106 structured to sense voltage and currentsignals 107 for each of the different electric loads 102, 103, 104, ahierarchical load feature database 108 having a plurality of layers (L1,L2, L3) 110, with a first layer 112 (L1) of the layers 110 including aplurality of different load categories; and a processor 114. Theprocessor 114 includes a routine 116, which in accordance with theteachings of the disclosed concept, acquires voltage and currentwaveforms from the sensors 106 for a corresponding one of the differentelectric loads 102, 103, 104; maps a voltage-current trajectory to agrid including a plurality of cells, each of which is assigned a binaryvalue of zero or one (see, for example, FIG. 3); extracts a plurality ofdifferent features from the mapped grid of cells as a graphicalsignature of the corresponding one of the different electric loads 102,103, 104; derives a category of the corresponding one of the differentelectric loads 102, 103, 104 from the database 108; and identifies oneof a plurality of different electric load types for the correspondingone of the different electric loads 102, 103, 104.

The major advantages of the proposed binary V-I feature extractioninclude reducing the harmonics and noise effects on load current andvoltage waveforms, providing a relatively simple abstraction ofgraphical shapes of trajectories, and simplifying graphical featureextraction.

The binary V-I features are relatively very easy to calculate, and takeless storage since the feature values are all integers. The initialcomputation and memory requirements have been evaluated, and the resultsshow that the computational cost of calculating the graphical featuresand the storage requirement is in the scale of x % of what is needed bya Fast Fourier Transform (FFT).

The disclosed concept employs a relatively low computational-cost, butyet accurate method and system, to extract signatures for electric loadidentification. Instead of utilizing digital signal processing andfrequency domain analysis, the disclosed concept employs the similarityof V-I trajectories between loads and maps V-I trajectories to a cellgrid with binary cell values. Graphical features are then extracted formany applications.

The disclosed concept significantly reduces the computational cost ascompared to existing frequency-domain feature extraction and analysistechnologies. Test results show that an average of over a 99% successrate can be achieved using the proposed signatures.

While specific embodiments of the disclosed concept have been describedin detail, it will be appreciated by those skilled in the art thatvarious modifications and alternatives to those details could bedeveloped in light of the overall teachings of the disclosure.Accordingly, the particular arrangements disclosed are meant to beillustrative only and not limiting as to the scope of the disclosedconcept which is to be given the full breadth of the claims appended andany and all equivalents thereof.

What is claimed is:
 1. A system for a plurality of different electricloads, the system comprising: a plurality of sensors structured to sensea voltage signal and a current signal for each of the different electricloads; a hierarchical load feature database comprising a plurality oflayers, with one of said layers including a plurality of different loadcategories; and a processor structured to: acquire a voltage waveformand a current waveform from the sensors for a corresponding one of thedifferent electric loads; map a voltage-current trajectory to a gridincluding a plurality of cells, each of said cells being assigned abinary value of zero or one based on whether the voltage-currenttrajectory passes through each of said cells; extract a plurality ofdifferent features from the mapped grid of cells as a graphicalsignature of the corresponding one of the different electric loads;derive a category of the corresponding one of the different electricloads from the hierarchical load feature database; and identify one of aplurality of different electric load types for the corresponding one ofthe different electric loads.
 2. The system of claim 1 wherein thehierarchical load feature database comprises three of said layers;wherein a first layer of said layers includes the different loadcategories; wherein a second layer of said layers includes a pluralityof different load sub-categories for each of said different loadcategories; and wherein a third layer of said layers includes thedifferent electric load types for said different load sub-categories. 3.The system of claim 1 wherein the different load categories includeresistive loads, reactive predominant loads, electronic loads with apower factor correction circuit, electronic loads without a power factorcorrection circuit, electric loads including a linear power supply usinga transformer to boost voltage, phase angle controllable loads, andcomplex structures.
 4. The system of claim 1 wherein said differentfeatures are eight different features.
 5. The system of claim 1 whereinsaid grid includes a first horizontal axis defining a count of saidcells and a second vertical axis defining said count of said cells;wherein said count is a positive plural integer N; wherein N=2Δ+1; andwherein said different features are selected from the group consistingof: (1) a binary value of one of the cells at a first location of thefirst horizontal axis and a location Δ+1 of the second vertical axis;(2) a binary value of one of the cells at a location Δ+1 of the firsthorizontal axis and the location Δ+1 of the second vertical axis; (3) abinary value determined by multiplication of binary values of all cellsalong a diagonal line in the grid from a lower left corner to an upperright corner to indicate whether the voltage-current trajectory islinear or non-linear; (4) a number of continuums of said cells withadjacent coordinates having a binary value of one within all of saidcells to indicate a number of intersections of the voltage-currenttrajectory and an average of a maximal value and a minimal value of thevoltage waveform corresponding to the first horizontal axis; (5) a countof zero or a number of self-crossing intersections of thevoltage-current trajectory; (6) a number of intersections of thevoltage-current trajectory with 1.3 times the average of the maximalvalue and the minimal value of the voltage waveform corresponding to thefirst horizontal axis; (7) existence of a central horizontalline-segment that occupies at least 30% of the entire first horizontalaxis and if at least 50% of the central horizontal line-segment overlapswith part of the voltage-current trajectory; and (8) a binary value ofone of the cells at the location Δ+1 of the first horizontal axis and afirst location of the second vertical axis.
 6. The system of claim 1wherein the voltage waveform and the current waveform each include atotal of K data points of the form (v_(k),i_(k)), wherein: k=1, . . . ,K; wherein v_(k) and i_(k) are a voltage value and a current value of asample data point k, respectively; wherein maximal and minimal values ofthe voltage waveform and the current waveform are computed from:v _(max)=maxv _(k),v _(min)=minv _(k),i _(max)=maxi _(k),i _(min)=mini _(k),${v_{0} = {\frac{1}{2}\left( {v_{\max} + v_{\min}} \right)}},{and}$${i_{0} = {\frac{1}{2}\left( {i_{\max} + i_{\min}} \right)}};$ whereinv₀ and i₀ are both averages of the corresponding maximal and minimalvalues, which form central points of the grid of said cells; wherein Δdefines size of said grid; wherein${\mathbb{d}v} = \frac{v_{\max} - v_{0}}{\Delta}$${{\mathbb{d}i} = \frac{i_{\max} - i_{0}}{\Delta}};$ wherein saidprocessor is further structured to generate two sequences from thevoltage waveform and the current waveform as:{v ₀ −dv·Δ,v ₀ −dv·(Δ−1), . . . ,v ₀ , . . . ,v ₀ +dv·(Δ−1),v ₀ +dv·Δ},and{i ₀ −di·Δ,i ₀ −di·(Δ−1), . . . ,i ₀ , . . . ,i ₀ +di·(Δ−1),i ₀ +di·Δ};wherein each of said two sequences has N=2Δ+1 elements; wherein the gridof said cells includes a first axis having N of said cells and a secondaxis having N of said cells; and wherein each of said cells is assigneda positional value (v₀+dv·x, i₀+di·y) and a binary model value B_(x,y),which is initialized to be
 0. 7. The system of claim 6 wherein saidprocessor is further structured to map one-half cycle of the voltagewaveform and the current waveform to the grid of said cells and toassign each of said K data points to a corresponding one of said cellswith said binary model value B_(x,y) of
 1. 8. The system of claim 1wherein said processor is further structured to determine a number ofself-crossing intersections contained by the mapped voltage-currenttrajectory.
 9. The system of claim 1 wherein the category of thecorresponding one of the different electric load types is derived from aSupervised Self-Organizing Map.
 10. The system of claim 1 wherein thecategory of the corresponding one of the different electric load typesis derived from a self-organizing map or a self-organizing feature maptrained using competitive learning.
 11. A method of identifying loadtypes for a plurality of different electric loads, said methodcomprising: sensing a voltage signal and a current signal for each ofthe different electric loads; providing a hierarchical load featuredatabase comprising a plurality of layers, with one of said layersincluding a plurality of different load categories; providing aprocessor; acquiring, with the processor, a voltage waveform and acurrent waveform for a corresponding one of the different electricloads; mapping, with the processor, a voltage-current trajectory to agrid including a plurality of cells, each of said cells being assigned abinary value of zero or one based on whether the voltage-currenttrajectory passes through each of said cells; extracting, with theprocessor, a plurality of different features from the mapped grid ofcells as a graphical signature of the corresponding one of the differentelectric loads; deriving, with the processor, a category of thecorresponding one of the different electric loads from the hierarchicalload feature database; and identifying, with the processor, one of aplurality of different electric load types for the corresponding one ofthe different electric loads.
 12. The method of claim 11 furthercomprising: employing three of said layers in said hierarchical loadfeature database; including the different load categories in a firstlayer of said layers; including a plurality of different loadsub-categories for each of said different load categories in a secondlayer of said layers; and including the different electric load typesfor said different load sub-categories in a third layer of said layers.13. The method of claim 11 further comprising: including resistiveloads, reactive predominant loads, electronic loads with a power factorcorrection circuit, electronic loads without a power factor correctioncircuit, electric loads including a linear power supply using atransformer to boost voltage, phase angle controllable loads, andcomplex structures as the different load categories.
 14. The method ofclaim 11 further comprising: employing as said different features eightdifferent features.
 15. The method of claim 11 further comprising:including a first horizontal axis of said grid defining a count of saidcells and a second vertical axis defining said count of said cells;employing a positive plural integer N as said count, with N=2Δ+1; andselecting said different features from the group consisting of: (1) abinary value of one of the cells at a first location of the firsthorizontal axis and a location Δ+1 of the second vertical axis; (2) abinary value of one of the cells at a location Δ+1 of the firsthorizontal axis and the location Δ+1 of the second vertical axis; (3) abinary value determined by multiplication of binary values of all cellsalong a diagonal line in the grid from a lower left corner to an upperright corner to indicate whether the voltage-current trajectory islinear or non-linear; (4) a number of continuums of said cells withadjacent coordinates having a binary value of one within all of saidcells to indicate a number of intersections of the voltage-currenttrajectory and an average of a maximal value and a minimal value of thevoltage waveform corresponding to the first horizontal axis; (5) a countof zero or a number of self-crossing intersections of thevoltage-current trajectory; (6) a number of intersections of thevoltage-current trajectory with 1.3 times the average of the maximalvalue and the minimal value of the voltage waveform corresponding to thefirst horizontal axis; (7) existence of a central horizontalline-segment that occupies at least 30% of the entire first horizontalaxis and if at least 50% of the central horizontal line-segment overlapswith part of the voltage-current trajectory; and (8) a binary value ofone of the cells at the location Δ+1 of the first horizontal axis and afirst location of the second vertical axis.
 16. The method of claim 11further comprising: including a total of K data points of the form(v_(k),i_(k)) with each of the voltage waveform and the currentwaveform, wherein: k=1, . . . , K; wherein v_(k) and i_(k) are a voltagevalue and a current value of a sample data point k, respectively;wherein maximal and minimal values of the voltage waveform and thecurrent waveform are computed from:v _(max)=maxv _(k),v _(min)=minv _(k),i _(max)=maxi _(k),i _(min)=mini _(k),${v_{0} = {\frac{1}{2}\left( {v_{\max} + v_{\min}} \right)}},{and}$${i_{0} = {\frac{1}{2}\left( {i_{\max} + i_{\min}} \right)}};$ whereinv₀ and i₀ are both averages of the corresponding maximal and minimalvalues, which form central points of the grid of said cells; wherein Δdefines size of said grid; wherein${\mathbb{d}v} = \frac{v_{\max} - v_{0}}{\Delta}$${{\mathbb{d}i} = \frac{i_{\max} - i_{0}}{\Delta}};$ and generating twosequences from the voltage waveform and the current waveform as:{v ₀ −dv·Δ,v ₀ −dv·(Δ−1), . . . ,v ₀ , . . . ,v ₀ +dv·(Δ−1),v ₀ +dv·Δ},and{i ₀ −di·Δ,i ₀ −di·(Δ−1), . . . ,i ₀ , . . . ,i ₀ +di·(Δ−1),i ₀ +di·Δ};wherein each of said two sequences has N=2Δ+1 elements; wherein the gridof said cells includes a first axis having N of said cells and a secondaxis having N of said cells; and wherein each of said cells is assigneda positional value (v₀+dv·x, i₀+di·y) and a binary model value B_(x,y),which is initialized to be
 0. 17. The method of claim 16 furthercomprising: mapping one-half cycle of the voltage waveform and thecurrent waveform to the grid of said cells; and assigning each of said Kdata points to a corresponding one of said cells with said binary modelvalue B_(x,y) of
 1. 18. The method of claim 11 further comprising:determining a number of self-crossing intersections contained by themapped voltage-current trajectory.
 19. The method of claim 11 furthercomprising: deriving the category of the corresponding one of thedifferent electric load types from a Supervised Self-Organizing Map. 20.The method of claim 11 further comprising: deriving the category of thecorresponding one of the different electric load types from aself-organizing map or a self-organizing feature map trained usingcompetitive learning.